{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f9a7d6a6",
   "metadata": {},
   "source": [
    "### 主代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1797260c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from langchain_community.chat_models import ChatOllama\n",
    "# from langchain_core.prompts import SystemMessagePromptTemplate,HumanMessagePromptTemplate,AIMessagePromptTemplate\n",
    "# from langchain_core.messages import (\n",
    "#     SystemMessage,\n",
    "#     AIMessage,\n",
    "#     HumanMessage\n",
    "# )\n",
    "# from langchain_core.output_parsers import StrOutputParser\n",
    "# from langchain_core.prompts import ChatPromptTemplate\n",
    "from langchain_community.llms import Ollama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed2a6c3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import argparse\n",
    "import json\n",
    "from tqdm import tqdm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a5f228f",
   "metadata": {},
   "outputs": [],
   "source": [
    "choices = [\"A\", \"B\", \"C\", \"D\"]  # 数据集结构是四选一"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92acb705",
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_subject(subject):\n",
    "    \"转化为无特殊字符格式\"\n",
    "    l = subject.split(\"_\")\n",
    "    s = \"\"\n",
    "    for entry in l:\n",
    "        s += \" \" + entry\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "293bc9a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_example(df, idx, include_answer=True):\n",
    "    # 格式化选项内容\n",
    "    prompt = df.iloc[idx, 0] # 问题\n",
    "    k = df.shape[1] - 2   # 4个选项\n",
    "    for j in range(k):\n",
    "        prompt += \"\\n{}. {}\".format(choices[j], df.iloc[idx, j+1])\n",
    "    prompt += \"\\nAnswer(must choose only one from [A,B,C,D], don't say anything else):\"\n",
    "    if include_answer:\n",
    "        prompt += \" {}\\n\\n\".format(df.iloc[idx, k + 1])\n",
    "    return prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "311e31f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_prompt(train_df, subject, k=-1):\n",
    "    prompt = \"The following are multiple choice questions (with answers) about {}.\\n\\n\".format(format_subject(subject))\n",
    "    if k == -1:\n",
    "        k = train_df.shape[0]  # 毕竟不知道训练集中每个科目的条目数\n",
    "    for i in range(k):\n",
    "        prompt += format_example(train_df, i)\n",
    "    return prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9794e44",
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval(args, subject, engine, dev_df, test_df):\n",
    "    \"\"\"\n",
    "    对当前subject利用model进行评测\n",
    "    \"\"\"\n",
    "    model = Ollama(model = engine+\":7b\",\n",
    "                  temperature=0,\n",
    "                  num_predict=1,\n",
    "                  top_k=1)\n",
    "    cors = []  # 正确率\n",
    "#     all_probs = []  # 各选项概率\n",
    "    answers = choices[:test_df.shape[1]-2]  # 四个选项\n",
    "    print(answers)\n",
    "    print(f\"subject: {subject}\")\n",
    "    for i in tqdm(range(test_df.shape[0]), desc=\"Generating\"):  # 原来为 \n",
    "        # get prompt and make sure it fits 利用few_shots进行产生prompt\n",
    "        k = args.ntrain   # 每个学科的测试数据集只有ntrain个条目，即5shots\n",
    "        prompt_end = format_example(test_df, i, include_answer=False)   # 这个是要模型回答的，不要答案\n",
    "        train_prompt = gen_prompt(dev_df, subject, k)  # ntrain_shots\n",
    "        prompt = train_prompt + prompt_end # 构成完整prompt\n",
    "\n",
    "#         while crop(prompt) != prompt:   # 防止一些shot不足情况\n",
    "#             k -= 1\n",
    "#             train_prompt = gen_prompt(dev_df, subject, k)\n",
    "#             prompt = train_prompt + prompt_end\n",
    "\n",
    "        label = test_df.iloc[i, test_df.shape[1]-1]  # 问题i的标注答案\n",
    " \n",
    "        while True:\n",
    "            try:\n",
    "                response = model.invoke(prompt)\n",
    "#                 print(response)\n",
    "                break\n",
    "                # 必须检验生成的是否是四个选项之一\n",
    "#                 if response in answers:\n",
    "#                     break\n",
    "#                 else:\n",
    "#                     print(response)\n",
    "#                     prompt = \"forget the memory before, and \" + prompt\n",
    "#                     continue\n",
    "            except Exception as e:\n",
    "                print(\"pausing\")\n",
    "                print(f\"ERROR Occuring: {e}\")\n",
    "                time.sleep(2)\n",
    "                continue\n",
    "\n",
    "#         lprobs = []\n",
    "        pred = response\n",
    "#         probs = softmax(np.array(lprobs))\n",
    "\n",
    "        cor = pred == label  # 是否回答正确\n",
    "        cors.append(cor)\n",
    "#         all_probs.append(probs)\n",
    "\n",
    "    acc = np.mean(cors)  # 本学科上的回答正确率\n",
    "    cors = np.array(cors)  # 正确回答的题号对应\n",
    "\n",
    "#     all_probs = np.array(all_probs)  # 每题回答正确的概率\n",
    "    print(\"Average accuracy {:.3f} - {}\".format(acc, subject))\n",
    "    \n",
    "    result_df = [{\n",
    "        \"subject\": subject,\n",
    "        \"acc\": acc,\n",
    "    }]\n",
    "    result_df = pd.DataFrame(result_df)\n",
    "    \n",
    "    csv_file_path = os.path.join(args.save_dir, f\"{engine}_results\", f'{engine}_subjects_total_eval_results.csv')\n",
    "    \n",
    "    if not os.path.exists(csv_file_path):\n",
    "        result_df.to_csv(csv_file_path, header=None, index=None)  # 创建文件并写入\n",
    "    else:\n",
    "        result_df.to_csv(csv_file_path, mode='a', header=None, index=None) # 数据追加\n",
    "        \n",
    "\n",
    "    return cors, acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd29bb56",
   "metadata": {},
   "outputs": [],
   "source": [
    "def main(info):\n",
    "    models = info.models\n",
    "#     print(models)\n",
    "    subjects = [f.split(\"_test.csv\")[0] for f in os.listdir(os.path.join(info.data_dir, \"test\"))]\n",
    "   # 保存结果文件夹创建 \n",
    "    if not os.path.exists(info.save_dir):\n",
    "        os.mkdir(info.save_dir)\n",
    "    \n",
    "    for model in models:\n",
    "        if not os.path.exists(os.path.join(info.save_dir, f\"{model}_results\")):\n",
    "            os.mkdir(os.path.join(info.save_dir, f\"{model}_results\"))\n",
    "    \n",
    "    # 对每个模型进行评估\n",
    "    for model in tqdm(models, desc=\"模型评测\"):\n",
    "        eval_cors = []  # 模型对所有学科的评测\n",
    "        print(f\"{model} is on Evoluation.\")\n",
    "        \n",
    "        for subject in tqdm(subjects, desc=\"subjects\"): # 删掉[:3]    # 自从第32个subject后每个都只回答20个吧，跑了两天了，受不了了\n",
    "            # 开发集\n",
    "            dev_df = pd.read_csv(os.path.join(info.data_dir, \"dev\", subject + \"_dev.csv\"), header=None)[:info.ntrain]\n",
    "            # 测试集\n",
    "            test_df = pd.read_csv(os.path.join(info.data_dir, \"test\", subject + \"_test.csv\"), header=None)\n",
    "            # 进行评测\n",
    "            cors, acc = eval(info, subject, model, dev_df, test_df)\n",
    "            eval_cors.append(cors)  # 本次学科评测\n",
    "#             temp_df = test_df[:20]\n",
    "#             temp_df[\"{}_ans_correct\".format(model)] = cors\n",
    "            test_df[\"{}_ans_correct\".format(model)] = cors  ##本与下行恢复，上两行删除\n",
    "            test_df.to_csv(os.path.join(info.save_dir, f\"{model}_results\", \"{}.csv\".format(subject)), header=None, index=None)\n",
    "#             temp_df.to_csv(os.path.join(info.save_dir, f\"{model}_results\", \"{}.csv\".format(subject)), header=None, index=None)\n",
    "        \n",
    "        weighted_acc = np.mean(np.concatenate(eval_cors)) # 在所有学科上的正确率\n",
    "        print(\"**\"*20)\n",
    "        print(f\"Average accuracy: {weighted_acc:.3f}\")\n",
    "\n",
    "        \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b5a16d11",
   "metadata": {},
   "outputs": [],
   "source": [
    "class My_Info:\n",
    "    def __init__(self,\n",
    "                ntrain:int = 5,  # 开发集实例数量\n",
    "                data_dir:str = \"data\",\n",
    "                save_dir:str = \"results\",\n",
    "                models:list = [\"qwen\"]):\n",
    "        self.ntrain = ntrain\n",
    "        self.data_dir = data_dir\n",
    "        self.save_dir = save_dir\n",
    "        self.models = models\n",
    "info = My_Info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4a1e4990",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "模型评测:   0%|                                                                                  | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "qwen is on Evoluation.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "subjects:   0%|                                                                                 | 0/26 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A', 'B', 'C', 'D']\n",
      "subject: high_school_world_history\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Generating:   0%|                                                                               | 0/20 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
      "\n",
      "Generating:   5%|███▌                                                                   | 1/20 [01:06<20:57, 66.18s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  10%|███████                                                                | 2/20 [01:21<10:49, 36.09s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  15%|██████████▋                                                            | 3/20 [01:41<08:09, 28.78s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  20%|██████████████▏                                                        | 4/20 [01:52<05:47, 21.73s/it]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "Generating:  30%|█████████████████████▎                                                 | 6/20 [02:24<04:21, 18.69s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  35%|████████████████████████▊                                              | 7/20 [02:41<03:54, 18.07s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  40%|████████████████████████████▍                                          | 8/20 [02:58<03:30, 17.58s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  45%|███████████████████████████████▉                                       | 9/20 [03:15<03:10, 17.34s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  50%|███████████████████████████████████                                   | 10/20 [03:37<03:08, 18.86s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  55%|██████████████████████████████████████▌                               | 11/20 [03:52<02:40, 17.78s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  60%|██████████████████████████████████████████                            | 12/20 [04:07<02:15, 16.98s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  65%|█████████████████████████████████████████████▌                        | 13/20 [04:22<01:54, 16.33s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [04:33<01:28, 14.67s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [04:52<01:20, 16.11s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [05:03<00:58, 14.54s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [05:14<00:40, 13.45s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [05:29<00:27, 13.94s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [05:49<00:15, 15.53s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [06:04<00:00, 18.23s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:   4%|██▋                                                                   | 1/26 [06:04<2:31:57, 364.70s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.700 - high_school_world_history\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: human_aging\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Generating:   0%|                                                                               | 0/20 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
      "\n",
      "Generating:   5%|███▌                                                                   | 1/20 [00:18<05:43, 18.08s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  10%|███████                                                                | 2/20 [00:24<03:26, 11.49s/it]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  30%|█████████████████████▎                                                 | 6/20 [00:48<01:34,  6.78s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  35%|████████████████████████▊                                              | 7/20 [00:55<01:27,  6.76s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  40%|████████████████████████████▍                                          | 8/20 [01:01<01:21,  6.79s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  45%|███████████████████████████████▉                                       | 9/20 [01:08<01:14,  6.78s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  50%|███████████████████████████████████                                   | 10/20 [01:15<01:07,  6.78s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  55%|██████████████████████████████████████▌                               | 11/20 [01:22<01:00,  6.77s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  60%|██████████████████████████████████████████                            | 12/20 [01:29<00:55,  6.90s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  65%|█████████████████████████████████████████████▌                        | 13/20 [01:36<00:48,  6.90s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [01:43<00:41,  6.88s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [01:49<00:34,  6.83s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [01:53<00:22,  5.75s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [01:59<00:18,  6.05s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:06<00:12,  6.23s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:13<00:06,  6.39s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:19<00:00,  6.99s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:   8%|█████▍                                                                | 2/26 [08:24<1:33:00, 232.51s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.600 - human_aging\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: human_sexuality\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Generating:   0%|                                                                               | 0/20 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
      "\n",
      "Generating:   5%|███▌                                                                   | 1/20 [00:18<05:45, 18.17s/it]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  30%|█████████████████████▎                                                 | 6/20 [00:51<01:42,  7.32s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  35%|████████████████████████▊                                              | 7/20 [00:58<01:32,  7.11s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  40%|████████████████████████████▍                                          | 8/20 [01:05<01:24,  7.01s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  45%|███████████████████████████████▉                                       | 9/20 [01:14<01:25,  7.76s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  50%|███████████████████████████████████                                   | 10/20 [01:21<01:14,  7.41s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  55%|██████████████████████████████████████▌                               | 11/20 [01:27<01:04,  7.17s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  60%|██████████████████████████████████████████                            | 12/20 [01:30<00:47,  5.94s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  65%|█████████████████████████████████████████████▌                        | 13/20 [01:37<00:43,  6.16s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [01:44<00:37,  6.31s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [01:47<00:26,  5.35s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [01:53<00:23,  5.76s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [02:00<00:18,  6.02s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:07<00:12,  6.20s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:13<00:06,  6.32s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:20<00:00,  7.02s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  12%|████████                                                              | 3/26 [10:45<1:13:00, 190.48s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.750 - human_sexuality\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: international_law\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  65%|█████████████████████████████████████████████▌                        | 13/20 [02:00<00:57,  8.28s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [02:07<00:47,  7.93s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [02:15<00:38,  7.67s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [02:22<00:29,  7.48s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [02:28<00:21,  7.30s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:35<00:14,  7.21s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:46<00:08,  8.10s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:56<00:00,  8.85s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  15%|██████████▊                                                           | 4/26 [13:42<1:07:53, 185.16s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.650 - international_law\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: jurisprudence\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [01:56<00:34,  6.82s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [02:07<00:31,  8.00s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [02:17<00:25,  8.53s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:24<00:16,  8.09s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:30<00:07,  7.71s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:34<00:00,  7.71s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  19%|█████████████▍                                                        | 5/26 [16:16<1:00:53, 173.99s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.750 - jurisprudence\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: logical_fallacies\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [01:56<00:34,  6.93s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [02:03<00:27,  6.91s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [02:10<00:20,  6.92s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:17<00:13,  6.90s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:24<00:06,  6.91s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:31<00:00,  7.56s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  23%|████████████████▌                                                       | 6/26 [18:47<55:25, 166.27s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.650 - logical_fallacies\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: machine_learning\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  45%|███████████████████████████████▉                                       | 9/20 [01:27<01:21,  7.39s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  50%|███████████████████████████████████                                   | 10/20 [01:33<01:12,  7.22s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  55%|██████████████████████████████████████▌                               | 11/20 [01:40<01:04,  7.14s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  60%|██████████████████████████████████████████                            | 12/20 [01:47<00:56,  7.06s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  65%|█████████████████████████████████████████████▌                        | 13/20 [01:54<00:49,  7.01s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [02:01<00:41,  6.97s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [02:04<00:29,  5.85s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [02:11<00:24,  6.18s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [02:18<00:19,  6.39s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:25<00:13,  6.57s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:32<00:06,  6.66s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:39<00:00,  7.97s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  27%|███████████████████▍                                                    | 7/26 [21:27<51:56, 164.04s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.350 - machine_learning\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: management\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [01:32<00:34,  5.70s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [01:39<00:30,  6.02s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [01:46<00:25,  6.26s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [01:49<00:15,  5.33s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [01:56<00:11,  5.74s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:02<00:06,  6.10s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:06<00:00,  6.31s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  31%|██████████████████████▏                                                 | 8/26 [23:33<45:36, 152.01s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.750 - management\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: marketing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [01:58<00:34,  6.87s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [02:05<00:27,  6.85s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [02:12<00:20,  6.87s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:19<00:13,  6.92s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:26<00:06,  6.92s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:33<00:00,  7.67s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  35%|████████████████████████▉                                               | 9/26 [26:06<43:11, 152.44s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.750 - marketing\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: medical_genetics\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [01:52<00:33,  6.77s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [01:59<00:27,  6.83s/it]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:12<00:13,  6.88s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:19<00:06,  6.89s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:23<00:00,  7.15s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  38%|███████████████████████████▎                                           | 10/26 [28:29<39:53, 149.57s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.700 - medical_genetics\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: miscellaneous\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [01:47<00:25,  6.40s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [01:54<00:19,  6.53s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [01:57<00:11,  5.54s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:00<00:04,  4.85s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:03<00:00,  6.20s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  42%|██████████████████████████████                                         | 11/26 [30:33<35:26, 141.75s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.650 - miscellaneous\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: moral_disputes\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Generating:   0%|                                                                               | 0/20 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "Generating:  30%|█████████████████████▎                                                 | 6/20 [01:00<01:53,  8.13s/it]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "Generating:  45%|███████████████████████████████▉                                       | 9/20 [01:18<01:11,  6.51s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  50%|███████████████████████████████████                                   | 10/20 [01:24<01:06,  6.62s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  55%|██████████████████████████████████████▌                               | 11/20 [01:34<01:08,  7.59s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  60%|██████████████████████████████████████████                            | 12/20 [01:41<00:59,  7.40s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  65%|█████████████████████████████████████████████▌                        | 13/20 [01:48<00:50,  7.28s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [01:55<00:43,  7.19s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [02:02<00:35,  7.14s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [02:09<00:28,  7.12s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [02:16<00:21,  7.08s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:27<00:16,  8.07s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:34<00:07,  7.71s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:40<00:00,  8.05s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  46%|████████████████████████████████▊                                      | 12/26 [33:14<34:26, 147.59s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.450 - moral_disputes\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: moral_scenarios\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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      "\n",
      "Generating:  30%|█████████████████████▎                                                 | 6/20 [01:06<01:57,  8.38s/it]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  55%|██████████████████████████████████████▌                               | 11/20 [01:41<01:04,  7.15s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  60%|██████████████████████████████████████████                            | 12/20 [01:48<00:56,  7.10s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  65%|█████████████████████████████████████████████▌                        | 13/20 [01:55<00:49,  7.04s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [02:02<00:42,  7.01s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [02:08<00:34,  6.98s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [02:16<00:28,  7.01s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [02:22<00:20,  6.99s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:29<00:13,  6.98s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:36<00:06,  6.98s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:43<00:00,  8.19s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  50%|███████████████████████████████████▌                                   | 13/26 [35:58<33:02, 152.53s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.400 - moral_scenarios\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: nutrition\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  55%|██████████████████████████████████████▌                               | 11/20 [01:36<01:06,  7.35s/it]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "Generating:  65%|█████████████████████████████████████████████▌                        | 13/20 [01:50<00:50,  7.16s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [01:57<00:42,  7.10s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [02:07<00:40,  8.20s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [02:14<00:31,  7.83s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [02:21<00:22,  7.57s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:32<00:16,  8.41s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:35<00:06,  6.87s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:38<00:00,  7.94s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  54%|██████████████████████████████████████▏                                | 14/26 [38:37<30:52, 154.41s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.700 - nutrition\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: philosophy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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      "\n",
      "Generating:  30%|█████████████████████▎                                                 | 6/20 [00:53<01:46,  7.63s/it]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [01:44<00:40,  6.73s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [01:51<00:33,  6.75s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [01:54<00:22,  5.69s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [02:01<00:18,  6.02s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:11<00:14,  7.16s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:18<00:07,  7.03s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:24<00:00,  7.24s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  58%|████████████████████████████████████████▉                              | 15/26 [41:02<27:47, 151.55s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.700 - philosophy\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: prehistory\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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      "Generating:   0%|                                                                               | 0/20 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  55%|██████████████████████████████████████▌                               | 11/20 [01:38<01:08,  7.62s/it]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [02:05<00:49,  8.24s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [02:12<00:39,  7.89s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [02:19<00:30,  7.60s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [02:26<00:22,  7.41s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:33<00:14,  7.25s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:40<00:07,  7.18s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:47<00:00,  8.38s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  62%|███████████████████████████████████████████▋                           | 16/26 [43:50<26:04, 156.41s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.600 - prehistory\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: professional_accounting\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  65%|█████████████████████████████████████████████▌                        | 13/20 [02:11<00:57,  8.20s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [02:22<00:54,  9.03s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [02:29<00:42,  8.43s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [02:36<00:31,  8.00s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [02:47<00:25,  8.66s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:58<00:18,  9.38s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [03:08<00:09,  9.81s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [03:19<00:00,  9.96s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  65%|██████████████████████████████████████████████▍                        | 17/26 [47:09<25:23, 169.31s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.250 - professional_accounting\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: professional_law\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  65%|█████████████████████████████████████████████▌                        | 13/20 [04:56<04:11, 35.90s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [05:08<02:50, 28.49s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [05:19<01:56, 23.28s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [05:30<01:18, 19.65s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [05:42<00:51, 17.14s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [05:58<00:33, 16.84s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [06:09<00:15, 15.20s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [06:20<00:00, 19.04s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  69%|█████████████████████████████████████████████████▏                     | 18/26 [53:30<31:03, 232.89s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.200 - professional_law\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: professional_medicine\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  65%|█████████████████████████████████████████████▌                        | 13/20 [03:13<01:22, 11.73s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [03:23<01:07, 11.30s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [03:34<00:55, 11.02s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [03:45<00:44, 11.22s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [04:02<00:38, 12.68s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [04:12<00:24, 12.13s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [04:33<00:14, 14.67s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [04:45<00:00, 14.26s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  73%|███████████████████████████████████████████████████▉                   | 19/26 [58:15<29:00, 248.64s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.500 - professional_medicine\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: professional_psychology\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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      "Generating:  25%|█████████████████▊                                                     | 5/20 [01:01<02:25,  9.72s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  30%|█████████████████████▎                                                 | 6/20 [01:08<02:04,  8.90s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  35%|████████████████████████▊                                              | 7/20 [01:15<01:49,  8.39s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  40%|████████████████████████████▍                                          | 8/20 [01:23<01:36,  8.04s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  45%|███████████████████████████████▉                                       | 9/20 [01:34<01:40,  9.11s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  50%|███████████████████████████████████                                   | 10/20 [01:42<01:25,  8.57s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  55%|██████████████████████████████████████▌                               | 11/20 [01:52<01:23,  9.23s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  60%|██████████████████████████████████████████                            | 12/20 [01:59<01:08,  8.60s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  65%|█████████████████████████████████████████████▌                        | 13/20 [02:07<00:57,  8.20s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [02:17<00:53,  8.95s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [02:28<00:46,  9.32s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [02:35<00:34,  8.72s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [02:42<00:24,  8.29s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:52<00:17,  8.85s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [03:04<00:09,  9.60s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [03:11<00:00,  9.58s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  77%|█████████████████████████████████████████████████████                | 20/26 [1:01:27<23:09, 231.53s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.550 - professional_psychology\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: public_relations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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      "Generating:   0%|                                                                               | 0/20 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  65%|█████████████████████████████████████████████▌                        | 13/20 [01:55<00:51,  7.31s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [02:02<00:43,  7.28s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [02:10<00:36,  7.24s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [02:17<00:29,  7.26s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [02:24<00:21,  7.21s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:31<00:14,  7.20s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:38<00:07,  7.18s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:50<00:00,  8.51s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  81%|███████████████████████████████████████████████████████▋             | 21/26 [1:04:17<17:45, 213.12s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.450 - public_relations\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: security_studies\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [03:13<01:06, 11.04s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [03:24<00:54, 10.86s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [03:35<00:44, 11.14s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [03:47<00:34, 11.35s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [04:03<00:25, 12.77s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [04:11<00:11, 11.16s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [04:27<00:00, 13.38s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  85%|██████████████████████████████████████████████████████████▍          | 22/26 [1:08:45<15:17, 229.50s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.350 - security_studies\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: sociology\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [01:58<00:46,  7.79s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [02:09<00:43,  8.62s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [02:16<00:32,  8.21s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [02:23<00:23,  7.93s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:30<00:15,  7.71s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:38<00:07,  7.56s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:45<00:00,  8.27s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  88%|█████████████████████████████████████████████████████████████        | 23/26 [1:11:30<10:30, 210.28s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.550 - sociology\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: us_foreign_policy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Generating:  60%|██████████████████████████████████████████                            | 12/20 [01:44<00:58,  7.37s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  65%|█████████████████████████████████████████████▌                        | 13/20 [01:51<00:51,  7.34s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [01:59<00:43,  7.32s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [02:06<00:36,  7.31s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [02:13<00:29,  7.29s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [02:20<00:21,  7.28s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:28<00:14,  7.27s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:35<00:07,  7.26s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:42<00:00,  8.12s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  92%|███████████████████████████████████████████████████████████████▋     | 24/26 [1:14:13<06:31, 195.95s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.850 - us_foreign_policy\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: virology\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Generating:   0%|                                                                               | 0/20 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
      "\n",
      "Generating:   5%|███▌                                                                   | 1/20 [00:19<06:15, 19.75s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  10%|███████                                                                | 2/20 [00:26<03:42, 12.37s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  15%|██████████▋                                                            | 3/20 [00:34<02:49,  9.96s/it]\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "Generating:  30%|█████████████████████▎                                                 | 6/20 [00:55<01:51,  7.93s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  35%|████████████████████████▊                                              | 7/20 [01:02<01:40,  7.70s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  40%|████████████████████████████▍                                          | 8/20 [01:10<01:30,  7.57s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  45%|███████████████████████████████▉                                       | 9/20 [01:17<01:22,  7.46s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  50%|███████████████████████████████████                                   | 10/20 [01:24<01:13,  7.40s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  55%|██████████████████████████████████████▌                               | 11/20 [01:31<01:06,  7.34s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  60%|██████████████████████████████████████████                            | 12/20 [01:39<00:58,  7.30s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  65%|█████████████████████████████████████████████▌                        | 13/20 [01:46<00:50,  7.27s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [01:53<00:43,  7.26s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [02:00<00:36,  7.25s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [02:08<00:28,  7.23s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [02:15<00:21,  7.25s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:22<00:14,  7.25s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:29<00:07,  7.26s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:37<00:00,  7.85s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects:  96%|██████████████████████████████████████████████████████████████████▎  | 25/26 [1:16:50<03:04, 184.29s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.400 - virology\n",
      "['A', 'B', 'C', 'D']\n",
      "subject: world_religions\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Generating:   0%|                                                                               | 0/20 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
      "\n",
      "Generating:   5%|███▌                                                                   | 1/20 [00:15<04:58, 15.70s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  10%|███████                                                                | 2/20 [00:22<03:12, 10.68s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  15%|██████████▋                                                            | 3/20 [00:30<02:34,  9.10s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  20%|██████████████▏                                                        | 4/20 [00:37<02:13,  8.34s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  25%|█████████████████▊                                                     | 5/20 [00:44<01:59,  7.94s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  30%|█████████████████████▎                                                 | 6/20 [00:51<01:47,  7.71s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  35%|████████████████████████▊                                              | 7/20 [00:58<01:37,  7.52s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  40%|████████████████████████████▍                                          | 8/20 [01:06<01:28,  7.42s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  45%|███████████████████████████████▉                                       | 9/20 [01:13<01:20,  7.36s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  50%|███████████████████████████████████                                   | 10/20 [01:20<01:13,  7.31s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  55%|██████████████████████████████████████▌                               | 11/20 [01:27<01:05,  7.27s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  60%|██████████████████████████████████████████                            | 12/20 [01:31<00:48,  6.10s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  65%|█████████████████████████████████████████████▌                        | 13/20 [01:38<00:45,  6.44s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  70%|█████████████████████████████████████████████████                     | 14/20 [01:45<00:39,  6.66s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  75%|████████████████████████████████████████████████████▌                 | 15/20 [01:48<00:28,  5.66s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  80%|████████████████████████████████████████████████████████              | 16/20 [01:52<00:19,  4.97s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  85%|███████████████████████████████████████████████████████████▌          | 17/20 [01:59<00:16,  5.65s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  90%|███████████████████████████████████████████████████████████████       | 18/20 [02:06<00:12,  6.12s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating:  95%|██████████████████████████████████████████████████████████████████▌   | 19/20 [02:13<00:06,  6.44s/it]\u001b[A\u001b[A\n",
      "\n",
      "Generating: 100%|██████████████████████████████████████████████████████████████████████| 20/20 [02:20<00:00,  7.05s/it]\u001b[A\u001b[A\n",
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\2872356793.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df[\"{}_ans_correct\".format(model)] = cors\n",
      "\n",
      "subjects: 100%|█████████████████████████████████████████████████████████████████████| 26/26 [1:19:11<00:00, 182.74s/it]\u001b[A\n",
      "模型评测: 100%|██████████████████████████████████████████████████████████████████████| 1/1 [1:19:11<00:00, 4751.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy 0.950 - world_religions\n",
      "****************************************\n",
      "Average accuracy: {weighted_acc:.3f}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "main(info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5001c439",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_result = pd.read_csv(os.path.join(info.save_dir, f\"{info.models[0]}_results\", f\"{info.models[0]}_subjects_total_eval_results.csv\"), header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "83841923",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_result.columns = [\"subjects\", \"acc\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "25f04b73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subjects</th>\n",
       "      <th>acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abstract_algebra</td>\n",
       "      <td>0.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>anatomy</td>\n",
       "      <td>0.459259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>astronomy</td>\n",
       "      <td>0.565789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>business_ethics</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>clinical_knowledge</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>college_biology</td>\n",
       "      <td>0.569444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>college_chemistry</td>\n",
       "      <td>0.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>college_computer_science</td>\n",
       "      <td>0.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>college_mathematics</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>college_medicine</td>\n",
       "      <td>0.526012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>college_physics</td>\n",
       "      <td>0.303922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>computer_security</td>\n",
       "      <td>0.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>conceptual_physics</td>\n",
       "      <td>0.459574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>econometrics</td>\n",
       "      <td>0.342105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>electrical_engineering</td>\n",
       "      <td>0.475862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>elementary_mathematics</td>\n",
       "      <td>0.296296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>formal_logic</td>\n",
       "      <td>0.341270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>global_facts</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>high_school_biology</td>\n",
       "      <td>0.593548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>high_school_chemistry</td>\n",
       "      <td>0.399015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>high_school_computer_science</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>high_school_european_history</td>\n",
       "      <td>0.660606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>high_school_geography</td>\n",
       "      <td>0.681818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>high_school_government_and_politics</td>\n",
       "      <td>0.751295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>high_school_macroeconomics</td>\n",
       "      <td>0.512821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>high_school_mathematics</td>\n",
       "      <td>0.188889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>high_school_microeconomics</td>\n",
       "      <td>0.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>high_school_physics</td>\n",
       "      <td>0.278146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>high_school_psychology</td>\n",
       "      <td>0.737615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>high_school_statistics</td>\n",
       "      <td>0.356481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>high_school_us_history</td>\n",
       "      <td>0.602941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>high_school_world_history</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>human_aging</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>human_sexuality</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>international_law</td>\n",
       "      <td>0.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>jurisprudence</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>logical_fallacies</td>\n",
       "      <td>0.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>machine_learning</td>\n",
       "      <td>0.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>management</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>marketing</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>medical_genetics</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>miscellaneous</td>\n",
       "      <td>0.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>moral_disputes</td>\n",
       "      <td>0.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>moral_scenarios</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>nutrition</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>philosophy</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>prehistory</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>professional_accounting</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>professional_law</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>professional_medicine</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>professional_psychology</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>public_relations</td>\n",
       "      <td>0.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>security_studies</td>\n",
       "      <td>0.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>sociology</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>us_foreign_policy</td>\n",
       "      <td>0.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>virology</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>world_religions</td>\n",
       "      <td>0.950000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               subjects       acc\n",
       "0                      abstract_algebra  0.390000\n",
       "1                               anatomy  0.459259\n",
       "2                             astronomy  0.565789\n",
       "3                       business_ethics  0.500000\n",
       "4                    clinical_knowledge  0.600000\n",
       "5                       college_biology  0.569444\n",
       "6                     college_chemistry  0.390000\n",
       "7              college_computer_science  0.360000\n",
       "8                   college_mathematics  0.250000\n",
       "9                      college_medicine  0.526012\n",
       "10                      college_physics  0.303922\n",
       "11                    computer_security  0.690000\n",
       "12                   conceptual_physics  0.459574\n",
       "13                         econometrics  0.342105\n",
       "14               electrical_engineering  0.475862\n",
       "15               elementary_mathematics  0.296296\n",
       "16                         formal_logic  0.341270\n",
       "17                         global_facts  0.250000\n",
       "18                  high_school_biology  0.593548\n",
       "19                high_school_chemistry  0.399015\n",
       "20         high_school_computer_science  0.500000\n",
       "21         high_school_european_history  0.660606\n",
       "22                high_school_geography  0.681818\n",
       "23  high_school_government_and_politics  0.751295\n",
       "24           high_school_macroeconomics  0.512821\n",
       "25              high_school_mathematics  0.188889\n",
       "26           high_school_microeconomics  0.588235\n",
       "27                  high_school_physics  0.278146\n",
       "28               high_school_psychology  0.737615\n",
       "29               high_school_statistics  0.356481\n",
       "30               high_school_us_history  0.602941\n",
       "31            high_school_world_history  0.700000\n",
       "32                          human_aging  0.600000\n",
       "33                      human_sexuality  0.750000\n",
       "34                    international_law  0.650000\n",
       "35                        jurisprudence  0.750000\n",
       "36                    logical_fallacies  0.650000\n",
       "37                     machine_learning  0.350000\n",
       "38                           management  0.750000\n",
       "39                            marketing  0.750000\n",
       "40                     medical_genetics  0.700000\n",
       "41                        miscellaneous  0.650000\n",
       "42                       moral_disputes  0.450000\n",
       "43                      moral_scenarios  0.400000\n",
       "44                            nutrition  0.700000\n",
       "45                           philosophy  0.700000\n",
       "46                           prehistory  0.600000\n",
       "47              professional_accounting  0.250000\n",
       "48                     professional_law  0.200000\n",
       "49                professional_medicine  0.500000\n",
       "50              professional_psychology  0.550000\n",
       "51                     public_relations  0.450000\n",
       "52                     security_studies  0.350000\n",
       "53                            sociology  0.550000\n",
       "54                    us_foreign_policy  0.850000\n",
       "55                             virology  0.400000\n",
       "56                      world_religions  0.950000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dcaabdc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_lis_dic = model_result.to_dict(orient=\"records\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "05dd9052",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_result = sorted(result_lis_dic, key=lambda x: x['acc'], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "35b0d1e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'subjects': 'world_religions', 'acc': 0.95},\n",
       " {'subjects': 'us_foreign_policy', 'acc': 0.85},\n",
       " {'subjects': 'high_school_government_and_politics',\n",
       "  'acc': 0.7512953367875648},\n",
       " {'subjects': 'human_sexuality', 'acc': 0.75},\n",
       " {'subjects': 'jurisprudence', 'acc': 0.75},\n",
       " {'subjects': 'management', 'acc': 0.75},\n",
       " {'subjects': 'marketing', 'acc': 0.75},\n",
       " {'subjects': 'high_school_psychology', 'acc': 0.7376146788990826},\n",
       " {'subjects': 'high_school_world_history', 'acc': 0.7},\n",
       " {'subjects': 'medical_genetics', 'acc': 0.7},\n",
       " {'subjects': 'nutrition', 'acc': 0.7},\n",
       " {'subjects': 'philosophy', 'acc': 0.7},\n",
       " {'subjects': 'computer_security', 'acc': 0.69},\n",
       " {'subjects': 'high_school_geography', 'acc': 0.6818181818181818},\n",
       " {'subjects': 'high_school_european_history', 'acc': 0.6606060606060606},\n",
       " {'subjects': 'international_law', 'acc': 0.65},\n",
       " {'subjects': 'logical_fallacies', 'acc': 0.65},\n",
       " {'subjects': 'miscellaneous', 'acc': 0.65},\n",
       " {'subjects': 'high_school_us_history', 'acc': 0.6029411764705882},\n",
       " {'subjects': 'clinical_knowledge', 'acc': 0.6},\n",
       " {'subjects': 'human_aging', 'acc': 0.6},\n",
       " {'subjects': 'prehistory', 'acc': 0.6},\n",
       " {'subjects': 'high_school_biology', 'acc': 0.5935483870967742},\n",
       " {'subjects': 'high_school_microeconomics', 'acc': 0.5882352941176471},\n",
       " {'subjects': 'college_biology', 'acc': 0.5694444444444444},\n",
       " {'subjects': 'astronomy', 'acc': 0.5657894736842105},\n",
       " {'subjects': 'professional_psychology', 'acc': 0.55},\n",
       " {'subjects': 'sociology', 'acc': 0.55},\n",
       " {'subjects': 'college_medicine', 'acc': 0.5260115606936416},\n",
       " {'subjects': 'high_school_macroeconomics', 'acc': 0.5128205128205128},\n",
       " {'subjects': 'business_ethics', 'acc': 0.5},\n",
       " {'subjects': 'high_school_computer_science', 'acc': 0.5},\n",
       " {'subjects': 'professional_medicine', 'acc': 0.5},\n",
       " {'subjects': 'electrical_engineering', 'acc': 0.4758620689655172},\n",
       " {'subjects': 'conceptual_physics', 'acc': 0.4595744680851064},\n",
       " {'subjects': 'anatomy', 'acc': 0.4592592592592592},\n",
       " {'subjects': 'moral_disputes', 'acc': 0.45},\n",
       " {'subjects': 'public_relations', 'acc': 0.45},\n",
       " {'subjects': 'moral_scenarios', 'acc': 0.4},\n",
       " {'subjects': 'virology', 'acc': 0.4},\n",
       " {'subjects': 'high_school_chemistry', 'acc': 0.3990147783251231},\n",
       " {'subjects': 'abstract_algebra', 'acc': 0.39},\n",
       " {'subjects': 'college_chemistry', 'acc': 0.39},\n",
       " {'subjects': 'college_computer_science', 'acc': 0.36},\n",
       " {'subjects': 'high_school_statistics', 'acc': 0.3564814814814814},\n",
       " {'subjects': 'machine_learning', 'acc': 0.35},\n",
       " {'subjects': 'security_studies', 'acc': 0.35},\n",
       " {'subjects': 'econometrics', 'acc': 0.3421052631578947},\n",
       " {'subjects': 'formal_logic', 'acc': 0.3412698412698413},\n",
       " {'subjects': 'college_physics', 'acc': 0.3039215686274509},\n",
       " {'subjects': 'elementary_mathematics', 'acc': 0.2962962962962963},\n",
       " {'subjects': 'high_school_physics', 'acc': 0.2781456953642384},\n",
       " {'subjects': 'college_mathematics', 'acc': 0.25},\n",
       " {'subjects': 'global_facts', 'acc': 0.25},\n",
       " {'subjects': 'professional_accounting', 'acc': 0.25},\n",
       " {'subjects': 'professional_law', 'acc': 0.2},\n",
       " {'subjects': 'high_school_mathematics', 'acc': 0.1888888888888888}]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0c2e6052",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\412675397.py:9: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  cmap = cm.get_cmap('coolwarm')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "# 提取横坐标和纵坐标\n",
    "subjects_lis = [item['subjects'] for item in sorted_result]\n",
    "acc_lis = [item['acc'] for item in sorted_result]\n",
    "\n",
    "# 创建颜色映射\n",
    "cmap = cm.get_cmap('coolwarm')\n",
    "bar_colors = [cmap(acc) for acc in acc_lis]\n",
    "\n",
    "# 绘制柱状图\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(subjects_lis, acc_lis, color=bar_colors)\n",
    "\n",
    "# 添加标题和标签\n",
    "plt.title('Subjects & Accuracy', fontsize=16)\n",
    "plt.xlabel('Subjects', fontsize=14)\n",
    "plt.ylabel('Accuracy', fontsize=14)\n",
    "plt.xticks(rotation=75, ha='right')\n",
    "# 显示网格\n",
    "plt.grid(axis='y')\n",
    "\n",
    "# 显示图形\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9503cda0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算数据集上的总体Acc\n",
    "subject_eval_result_dir = os.listdir(os.path.join(info.save_dir, f\"{info.models[0]}_results\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7dc911bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 筛选 \n",
    "\n",
    "subject_eval_result_dir = [sub_result for sub_result in subject_eval_result_dir if \".csv\" in sub_result]\n",
    "subject_eval_result_dir.remove(f\"{info.models[0]}_subjects_total_eval_results.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ed2274c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['abstract_algebra.csv',\n",
       " 'anatomy.csv',\n",
       " 'astronomy.csv',\n",
       " 'business_ethics.csv',\n",
       " 'clinical_knowledge.csv',\n",
       " 'college_biology.csv',\n",
       " 'college_chemistry.csv',\n",
       " 'college_computer_science.csv',\n",
       " 'college_mathematics.csv',\n",
       " 'college_medicine.csv',\n",
       " 'college_physics.csv',\n",
       " 'computer_security.csv',\n",
       " 'conceptual_physics.csv',\n",
       " 'econometrics.csv',\n",
       " 'electrical_engineering.csv',\n",
       " 'elementary_mathematics.csv',\n",
       " 'formal_logic.csv',\n",
       " 'global_facts.csv',\n",
       " 'high_school_biology.csv',\n",
       " 'high_school_chemistry.csv',\n",
       " 'high_school_computer_science.csv',\n",
       " 'high_school_european_history.csv',\n",
       " 'high_school_geography.csv',\n",
       " 'high_school_government_and_politics.csv',\n",
       " 'high_school_macroeconomics.csv',\n",
       " 'high_school_mathematics.csv',\n",
       " 'high_school_microeconomics.csv',\n",
       " 'high_school_physics.csv',\n",
       " 'high_school_psychology.csv',\n",
       " 'high_school_statistics.csv',\n",
       " 'high_school_us_history.csv',\n",
       " 'high_school_world_history.csv',\n",
       " 'human_aging.csv',\n",
       " 'human_sexuality.csv',\n",
       " 'international_law.csv',\n",
       " 'jurisprudence.csv',\n",
       " 'logical_fallacies.csv',\n",
       " 'machine_learning.csv',\n",
       " 'management.csv',\n",
       " 'marketing.csv',\n",
       " 'medical_genetics.csv',\n",
       " 'miscellaneous.csv',\n",
       " 'moral_disputes.csv',\n",
       " 'moral_scenarios.csv',\n",
       " 'nutrition.csv',\n",
       " 'philosophy.csv',\n",
       " 'prehistory.csv',\n",
       " 'professional_accounting.csv',\n",
       " 'professional_law.csv',\n",
       " 'professional_medicine.csv',\n",
       " 'professional_psychology.csv',\n",
       " 'public_relations.csv',\n",
       " 'security_studies.csv',\n",
       " 'sociology.csv',\n",
       " 'us_foreign_policy.csv',\n",
       " 'virology.csv',\n",
       " 'world_religions.csv']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subject_eval_result_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "20ff1eef",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_corrs = []\n",
    "for sub_eval_result in subject_eval_result_dir:\n",
    "    content = pd.read_csv(os.path.join(info.save_dir, f\"{info.models[0]}_results\", sub_eval_result), header=None)\n",
    "    all_corrs.append(content[content.shape[1] - 1].to_list())                                             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "1f15e9ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_corrs = np.concatenate(all_corrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "70d1a9dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "weighted_acc = np.mean(all_corrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ebf1ee63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5034526051475204"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weighted_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "6d701f2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\1226076429.py:9: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  cmap = cm.get_cmap('coolwarm')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "# 提取横坐标和纵坐标\n",
    "subjects_lis = [item['subjects'] for item in sorted_result]\n",
    "acc_lis = [item['acc'] for item in sorted_result]\n",
    "\n",
    "# 创建颜色映射\n",
    "cmap = cm.get_cmap('coolwarm')\n",
    "bar_colors = [cmap(acc) for acc in acc_lis]\n",
    "\n",
    "# 绘制柱状图\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(subjects_lis, acc_lis, color=bar_colors)\n",
    "\n",
    "# 添加标题和标签\n",
    "plt.title('Subjects & Accuracy', fontsize=16)\n",
    "plt.xlabel('Subjects', fontsize=14)\n",
    "plt.ylabel('Accuracy', fontsize=14)\n",
    "plt.xticks(rotation=75, ha='right')\n",
    "\n",
    "# 绘制水平虚线\n",
    "threshold = weighted_acc\n",
    "plt.axhline(y=threshold, color='grey', linestyle='--', linewidth=1)\n",
    "\n",
    "# 在纵轴上标出0.39并加粗\n",
    "plt.text(-12, threshold, f\"{threshold:.4}\", color='red', fontsize=12, weight='bold', va='center')\n",
    "\n",
    "# 显示网格\n",
    "# plt.grid(axis='y')\n",
    "\n",
    "# 显示图形\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d67305a3",
   "metadata": {},
   "source": [
    "##  评估模型在每个大类学科的能力"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "29ed2bdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 评估模型在每个大类学科的能力\n",
    "from categories import subcategories, categories\n",
    "# subcategories.items()\n",
    "sub_cts = []\n",
    "subs = []\n",
    "for item in subcategories.items():\n",
    "#     print(f\"{item[0]}: {item[1][0]}\")\n",
    "    sub_cts.append(item[1][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "fb6e6dff",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_cts_dict = {subject: [] for subject in set(sub_cts)}   # 构建sub_cts：subs_list 结构字典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d61397df",
   "metadata": {},
   "outputs": [],
   "source": [
    "for item in subcategories.items():\n",
    "    sub_cts_dict[item[1][0]].append(item[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "12e1214c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算subcategories的评测结果\n",
    "sub_cts_result = {subject: 0 for subject in set(sub_cts)}    #{\"math\":[  ,  ,  ,文件名]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "45acc636",
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in sub_cts_dict.keys():\n",
    "    result_csv_files = sub_cts_dict[k]\n",
    "#     print(result_csv_files)\n",
    "    sub_corrs = []\n",
    "    for sub in result_csv_files:\n",
    "        sub_content = pd.read_csv(os.path.join(info.save_dir, f\"{info.models[0]}_results\", sub+\".csv\"), header=None)\n",
    "        sub_corrs.append(sub_content[content.shape[1] - 1].to_list())\n",
    "    sub_corrs = np.concatenate(sub_corrs)\n",
    "    sub_weighted_acc = np.mean(sub_corrs)\n",
    "    sub_cts_result[k] = sub_weighted_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "6c8a9f9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'economics': 0.5107816711590296,\n",
       " 'politics': 0.7035573122529645,\n",
       " 'philosophy': 0.4690265486725664,\n",
       " 'other': 0.30714285714285716,\n",
       " 'psychology': 0.7309734513274336,\n",
       " 'chemistry': 0.39603960396039606,\n",
       " 'computer science': 0.50625,\n",
       " 'business': 0.5714285714285714,\n",
       " 'law': 0.5333333333333333,\n",
       " 'culture': 0.65,\n",
       " 'history': 0.6308068459657702,\n",
       " 'geography': 0.6818181818181818,\n",
       " 'engineering': 0.47586206896551725,\n",
       " 'physics': 0.4171875,\n",
       " 'math': 0.2857142857142857,\n",
       " 'biology': 0.5859030837004405,\n",
       " 'health': 0.549777117384844}"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_cts_result  # 对所有subject_category的评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "a262dfab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\636582300.py:16: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  cmap = cm.get_cmap('coolwarm')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "sub_cts_result = sorted(sub_cts_result.items(), key=lambda item: item[1], reverse=True)\n",
    "sub_cts_result = dict(sub_cts_result)\n",
    "\n",
    "sub_cts_lis = []\n",
    "sub_cts_eval_lis = []\n",
    "\n",
    "# 提取横坐标和纵坐标\n",
    "for item in sub_cts_result.items():\n",
    "    sub_cts_lis.append(item[0])\n",
    "    sub_cts_eval_lis.append(item[1])\n",
    "\n",
    "# 创建颜色映射\n",
    "cmap = cm.get_cmap('coolwarm')\n",
    "sub_cts_bar_colors = [cmap(sub_cts_eval) for sub_cts_eval in sub_cts_eval_lis]\n",
    "\n",
    "# 绘制柱状图\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(sub_cts_lis, sub_cts_eval_lis, color=sub_cts_bar_colors)\n",
    "\n",
    "# 添加标题和标签\n",
    "plt.title('SubCategory & Accuracy', fontsize=16)\n",
    "plt.xlabel('SubCategory', fontsize=14)\n",
    "plt.ylabel('Accuracy', fontsize=14)\n",
    "plt.xticks(rotation=75, ha='right')\n",
    "\n",
    "# 绘制水平虚线\n",
    "threshold = weighted_acc\n",
    "plt.axhline(y=threshold, color='grey', linestyle='--', linewidth=1)\n",
    "\n",
    "# 在纵轴上标出0.39并加粗\n",
    "plt.text(-3.5, threshold, f\"{threshold:.4}\", color='red', fontsize=12, weight='bold', va='center')\n",
    "\n",
    "# 显示网格\n",
    "# plt.grid(axis='y')\n",
    "\n",
    "# 显示图形\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "3285077f",
   "metadata": {},
   "outputs": [],
   "source": [
    "categories_dict = {cts[0]: [] for cts in categories.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "4b2a8602",
   "metadata": {},
   "outputs": [],
   "source": [
    "for item in categories.items():   #(\"STEM\",[ \"MATH\", , ])\n",
    "    for sub_item in item[1]:\n",
    "        categories_dict[item[0]].append(sub_cts_dict[sub_item])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "ec0c838e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in categories_dict.keys():\n",
    "    categories_dict[k] = np.concatenate(categories_dict[k])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "498ca702",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算categories评估结果\n",
    "cts_result = {key: 0 for key in categories_dict.keys()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "731c8089",
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in categories_dict.keys():\n",
    "    result_csv_files = categories_dict[k]\n",
    "#     print(result_csv_files)\n",
    "    cts_corrs = []\n",
    "    for sub in result_csv_files:\n",
    "        sub_content = pd.read_csv(os.path.join(info.save_dir, f\"{info.models[0]}_results\", sub+\".csv\"), header=None)\n",
    "        cts_corrs.append(sub_content[content.shape[1] - 1].to_list())\n",
    "    cts_corrs = np.concatenate(cts_corrs)\n",
    "    cts_weighted_acc = np.mean(cts_corrs)\n",
    "    cts_result[k] = cts_weighted_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "9365982f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'STEM': 0.40601503759398494,\n",
       " 'humanities': 0.5697841726618705,\n",
       " 'social sciences': 0.6290322580645161,\n",
       " 'other (business, health, misc.)': 0.5173137460650578}"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cts_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "7c738052",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\20931\\AppData\\Local\\Temp\\ipykernel_25456\\3475915206.py:16: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  cmap = cm.get_cmap('coolwarm')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "cts_result = sorted(cts_result.items(), key=lambda item: item[1], reverse=True)\n",
    "cts_result = dict(cts_result)\n",
    "\n",
    "cts_lis = []\n",
    "cts_eval_lis = []\n",
    "\n",
    "# 提取横坐标和纵坐标\n",
    "for item in cts_result.items():\n",
    "    cts_lis.append(item[0])\n",
    "    cts_eval_lis.append(item[1])\n",
    "\n",
    "# 创建颜色映射\n",
    "cmap = cm.get_cmap('coolwarm')\n",
    "cts_bar_colors = [cmap(cts_eval) for cts_eval in cts_eval_lis]\n",
    "\n",
    "# 绘制柱状图\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(cts_lis, cts_eval_lis, color=cts_bar_colors)\n",
    "\n",
    "# 添加标题和标签\n",
    "plt.title('Category & Accuracy', fontsize=16)\n",
    "plt.xlabel('Category', fontsize=14)\n",
    "plt.ylabel('Accuracy', fontsize=14)\n",
    "plt.xticks(rotation=75, ha='right')\n",
    "\n",
    "# 绘制水平虚线\n",
    "threshold = weighted_acc\n",
    "plt.axhline(y=threshold, color='grey', linestyle='--', linewidth=1)\n",
    "\n",
    "# 在纵轴上标出0.39并加粗\n",
    "plt.text(-1.1, threshold, f\"{threshold:.4}\", color='red', fontsize=12, weight='bold', va='center')\n",
    "\n",
    "# 显示网格\n",
    "# plt.grid(axis='y')\n",
    "\n",
    "# 显示图形\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "c731a703",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = cts_eval_lis\n",
    "features = cts_lis\n",
    "\n",
    "# 计算每个特征的角度\n",
    "angles = np.linspace(0, 2 * np.pi, len(features), endpoint=False).tolist()\n",
    "\n",
    "# 将雷达图的起点与终点连接\n",
    "values += values[:1]\n",
    "angles += angles[:1]\n",
    "baseline = weighted_acc\n",
    "# 基准线的数据\n",
    "baseline_values = [baseline] * len(features)\n",
    "baseline_values += [baseline]  # 闭合基准线\n",
    "\n",
    "# 绘制雷达图\n",
    "fig, ax = plt.subplots(figsize=(8, 6), subplot_kw=dict(polar=True))\n",
    "\n",
    "# 绘制数据线\n",
    "ax.fill(angles, values, color='blue', alpha=0.25, label='Values')\n",
    "ax.plot(angles, values, color='blue', linewidth=2)\n",
    "\n",
    "# 绘制基准线\n",
    "ax.fill(angles, baseline_values, color='orange', alpha=0.5, label=f'Baseline {baseline:.3}')\n",
    "ax.plot(angles, baseline_values, color='orange', linewidth=2, linestyle='--')\n",
    "\n",
    "# 添加特征标签\n",
    "ax.set_yticklabels([])\n",
    "ax.set_xticks(angles[:-1])\n",
    "ax.set_xticklabels(features, fontsize=12)\n",
    "\n",
    "# 添加图例\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(0.1, 0.1))\n",
    "\n",
    "# 添加标题\n",
    "plt.title('Category Radar with Baseline', fontsize=16)\n",
    "\n",
    "# 显示图形\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:LangChain_and_VectorDB]",
   "language": "python",
   "name": "conda-env-LangChain_and_VectorDB-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
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